Enhancing the Conductivity and Dielectric Characteristics of Bismuth Oxyiodide via Activated Carbon Doping.

Activated carbon/BiOI nanocomposites were successfully synthesized through a simplistic method. The produced composites were then characterized using XRD, TEM, SEM-EDX, and XPS. The results showed that BiOI with a tetragonal crystal structure had been formed. The interaction between activated carbon and BiOI was confirmed via all the mentioned tools. The obtained nanocomposites' electrical conductivity, dielectric properties, and Ac impedance were studied at 59 KHz-1.29 MHz. AC and dc conductivities were studied at temperatures between 303 and 573 K within the frequency range of 59 KHz-1.29 MHz. The 10% activated carbon/BiOI nanocomposite possessed dc and AC conductivity values of 5.56 × 10-4 and 2.86 × 10-4 Ω-1.cm-1, respectively, which were higher than BiOI and the other nanocomposites. Every sample exhibited increased electrical conductivity values as the temperature and frequency rose, suggesting that all samples had semiconducting behavior. The loss and dielectric constants (ε' and ε″) also dropped as the frequency increased, leading to higher dielectric loss. The Nyquist plot unraveled single semicircle arcs and a decreased bulk resistance, indicating decreased grain boundary resistance. Consequently, the electrical characteristics of BiOI, 1C/BiOI, 5C/BiOI, and 10C/BiOI implied their applicability as dielectric absorbers, charge-stored capacitors, and high-frequency microwave devices.

Therapeutic benefits of quercetin in traumatic brain injury model exposed to cigarette smoke.

Scientific evidences reported the deleterious effect of cigarette smoking or passive smoking on brain health particularly cognitive functions, blood-brain barrier (BBB) permeability, up-regulation of inflammatory cascades, and depletion of the antioxidant system. These combined effects become more progressive in the events of stroke, traumatic brain injury (TBI), and many other neurodegenerative diseases. In the current study, we investigated the long-term administered therapeutic potential of quercetin in ameliorating the deleterious neurobiological consequences of chronic tobacco smoke exposure in TBI mice. After exposure to 21 days of cigarette smoke and treatment with 50 mg/kg of quercetin, C57BL/6 mice were challenged for the induction of TBI by the weight drop method. Subsequently, a battery of behavioral tests and immunohistochemical analyses revealed the beneficial effect of quercetin on the locomotive and cognitive function of TBI + smoked group mice (p < 0.05 vs control sham). Immunohistochemistry analysis (Nrf2, HO-1, NFkB, caspase 3) demonstrated a marked protection after 21 days of quercetin treatment in the chronic tobacco smoking group possibly by up-regulation of antioxidant pathways, and decreased apoptosis. In conclusion, our findings support the therapeutic effectiveness of quercetin in partly protecting the central neurological functions that become aberrantly impaired in combined habitual cigarette-smoking individuals impacted with TBI.

Molecular Property Prediction of Modified Gedunin Using Machine Learning.

Images of molecules are often utilized in education and synthetic exploration to predict molecular characteristics. Deep learning (DL) has also had an influence on drug research, such as the interpretation of cellular images as well as the development of innovative methods for the synthesis of organic molecules. Although research in these areas has been significant, a comprehensive review of DL applications in drug development would be beyond the scope of a single Account. In this study, we will concentrate on a single major area where DL has influenced molecular design: the prediction of molecular properties of modified gedunin using machine learning (ML). AI and ML technologies are critical in drug research and development. In these other words, deep learning (DL) algorithms and artificial neural networks (ANN) have changed the field. In short, advances in AI and ML present a good potential for rational drug design and exploration, which will ultimately benefit humanity. In this paper, long short-term memory (LSTM) was used to convert a modified gedunin SMILE into a molecular image. The 2D molecular representations and their immediately visible highlights should then provide adequate data to predict the subordinate characteristics of atom design. We aim to find the properties of modified gedunin using K-means clustering; RNN-like ML tools. To support this postulation, neural network (NN) clustering based on the AI picture is used and evaluated in this study. The novel chemical developed via profound learning has long been predicted on characteristics. As a result, LSTM with RNNs allow us to predict the properties of molecules of modified gedunin. The total accuracy of the suggested model is 98.68%. The accuracy of the molecular property prediction of modified gedunin research is promising enough to evaluate extrapolation and generalization. The model suggested in this research requires just seconds or minutes to calculate, making it faster as well as more effective than existing techniques. In short, ML can be a useful tool for predicting the properties of modified gedunin molecules.

Identifying the alpha-glucosidase inhibitory potential of dietary phytochemicals against diabetes mellitus type 2 via molecular interactions and dynamics simulation.

The research aims to identify the inhibitory potential of natural dietary phytochemicals against non-insulinotropic target protein alpha-glucosidase and its possible implications to diabetes mellitus type 2. A data set of sixteen plant-derived dietary molecules viz., 4,5-dimethyl-3-hydroxy-2(5H)-furanone, apigenin, bromelain, caffeic acid, cholecalciferol, dihydrokaempferol 7-o-glucopyranoside, galactomannan, genkwanin, isoimperatorin, luteolin, luteolin 7-o-glucoside, neohesperidin, oleanoic acid, pelargonidin-3-rutinoside, quercetin, and quinic acid were taken to accomplish molecular docking succeeded by their comparison with known inhibitors including acarbose, miglitol, voglibose, emiglitate, and 1-deoxynojirimycin. Among all phyto-compounds, bromelain (ΔG: -9.54 kcal/mol), cholecalciferol (-8.47 kcal/mol), luteolin (-9.02 kcal/mol), and neohesperidin (-8.53 kcal/mol) demonstrated better binding interactions with alpha-glucosidase in comparison to the best-known inhibitor, acarbose (ΔG: -7.93 kcal/mol). Molecular dynamics simulation of 10 ns duration, CYP450 site of metabolism identification, and prediction of activity spectra for substances depicted the bromelain as the most stable inhibitor compared to luteolin and acarbose. Findings of molecular interactions, molecular dynamics study, metabolism, and biological activity prediction proved bromelain as a potential alpha-glucosidase inhibitor. Thus, bromelain might be helpful as an insulin-independent therapeutic molecule towards controlling and managing diabetes mellitus type 2.

Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique.

Cardiac arrhythmias pose a significant danger to human life; therefore, it is of utmost importance to be able to efficiently diagnose these arrhythmias promptly. There exist many techniques for the detection of arrhythmias; however, the most widely adopted method is the use of an Electrocardiogram (ECG). The manual analysis of ECGs by medical experts is often inefficient. Therefore, the detection and recognition of ECG characteristics via machine-learning techniques have become prevalent. There are two major drawbacks of existing machine-learning approaches: (a) they require extensive training time; and (b) they require manual feature selection. To address these issues, this paper presents a novel deep-learning framework that integrates various networks by stacking similar layers in each network to produce a single robust model. The proposed framework has been tested on two publicly available datasets for the recognition of five micro-classes of arrhythmias. The overall classification sensitivity, specificity, positive predictive value, and accuracy of the proposed approach are 98.37%, 99.59%, 98.41%, and 99.35%, respectively. The results are compared with state-of-the-art approaches. The proposed approach outperformed the existing approaches in terms of sensitivity, specificity, positive predictive value, accuracy and computational cost.

Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System.

Human activity monitoring is a fascinating area of research to support autonomous living in the aged and disabled community. Cameras, sensors, wearables, and non-contact microwave sensing have all been suggested in the past as methods for identifying distinct human activities. Microwave sensing is an approach that has lately attracted much interest since it has the potential to address privacy problems caused by cameras and discomfort caused by wearables, especially in the healthcare domain. A fundamental drawback of the current microwave sensing methods such as radar is non-line-of-sight and multi-floor environments. They need precise and regulated conditions to detect activity with high precision. In this paper, we have utilised the publicly available online database based on the intelligent reflecting surface (IRS) system developed at the Communications, Sensing and Imaging group at the University of Glasgow, UK (references 39 and 40). The IRS system works better in the multi-floor and non-line-of-sight environments. This work for the first time uses algorithms such as support vector machine Bagging and Decision Tree on the publicly available IRS data and achieves better accuracy when a subset of the available data is considered along specific human activities. Additionally, the work also considers the processing time taken by the classier in training stage when exposed to the IRS data which was not previously explored.

Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model.

This article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive coupled with security preservation. Sensing and securing healthcare and medical images such as X-rays that can be used to diagnose viral diseases such as pneumonia is a challenging task for researchers. In the past few years, patients' medical records have been shared using various wireless technologies. The wireless transmitted data are prone to attacks, resulting in the misuse of patients' medical records. Therefore, it is important to secure medical data, which are in the form of images. The proposed work is divided into two sections: in the first section, primary data in the form of images are encrypted using the proposed technique based on chaos and convolution neural network. Furthermore, multiple chaotic maps are incorporated to create a random number generator, and the generated random sequence is used for pixel permutation and substitution. In the second part of the proposed work, a new technique for pneumonia diagnosis using deep learning, in which X-ray images are used as a dataset, is proposed. Several physiological features such as cough, fever, chest pain, flu, low energy, sweating, shaking, chills, shortness of breath, fatigue, loss of appetite, and headache and statistical features such as entropy, correlation, contrast dissimilarity, etc., are extracted from the X-ray images for the pneumonia diagnosis. Moreover, machine learning algorithms such as support vector machines, decision trees, random forests, and naive Bayes are also implemented for the proposed model and compared with the proposed CNN-based model. Furthermore, to improve the CNN-based proposed model, transfer learning and fine tuning are also incorporated. It is found that CNN performs better than other machine learning algorithms as the accuracy of the proposed work when using naive Bayes and CNN is 89% and 97%, respectively, which is also greater than the average accuracy of the existing schemes, which is 90%. Further, K-fold analysis and voting techniques are also incorporated to improve the accuracy of the proposed model. Different metrics such as entropy, correlation, contrast, and energy are used to gauge the performance of the proposed encryption technology, while precision, recall, F1 score, and support are used to evaluate the effectiveness of the proposed machine learning-based model for pneumonia diagnosis. The entropy and correlation of the proposed work are 7.999 and 0.0001, respectively, which reflects that the proposed encryption algorithm offers a higher security of the digital data. Moreover, a detailed comparison with the existing work is also made and reveals that both the proposed models work better than the existing work.

Multiple Participants' Discrete Activity Recognition in a Well-Controlled Environment Using Universal Software Radio Peripheral Wireless Sensing.

Wireless sensing is the utmost cutting-edge way of monitoring different health-related activities and, concurrently, preserving most of the privacy of individuals. To meet future needs, multi-subject activity monitoring is in demand, whether it is for smart care centres or homes. In this paper, a smart monitoring system for different human activities is proposed based on radio-frequency sensing integrated with ensemble machine learning models. The ensemble technique can recognise a wide range of activity based on alterations in the wireless signal's Channel State Information (CSI). The proposed system operates at 3.75 GHz, and up to four subjects participated in the experimental study in order to acquire data on sixteen distinct daily living activities: sitting, standing, and walking. The proposed methodology merges subject count and performed activities, resulting in occupancy count and activity performed being recognised at the same time. To capture alterations owing to concurrent multi-subject motions, the CSI amplitudes collected from 51 subcarriers of the wireless signals were processed and merged. To distinguish multi-subject activity, a machine learning model based on an ensemble learning technique was designed and trained using the acquired CSI data. For maximum activity classes, the proposed approach attained a high average accuracy of up to 98%. The presented system has the ability to fulfil prospective health activity monitoring demands and is a viable solution towards well-being tracking.

Thiazole: A Versatile Standalone Moiety Contributing to the Development of Various Drugs and Biologically Active Agents.

For many decades, the thiazole moiety has been an important heterocycle in the world of chemistry. The thiazole ring consists of sulfur and nitrogen in such a fashion that the pi (π) electrons are free to move from one bond to other bonds rendering aromatic ring properties. On account of its aromaticity, the ring has many reactive positions where donor-acceptor, nucleophilic, oxidation reactions, etc., may take place. Molecules containing a thiazole ring, when entering physiological systems, behave unpredictably and reset the system differently. These molecules may activate/stop the biochemical pathways and enzymes or stimulate/block the receptors in the biological systems. Therefore, medicinal chemists have been focusing their efforts on thiazole-bearing compounds in order to develop novel therapeutic agents for a variety of pathological conditions. This review attempts to inform the readers on three major classes of thiazole-bearing molecules: Thiazoles as treatment drugs, thiazoles in clinical trials, and thiazoles in preclinical and developmental stages. A compilation of preclinical and developmental thiazole-bearing molecules is presented, focusing on their brief synthetic description and preclinical studies relating to structure-based activity analysis. The authors expect that the current review may succeed in drawing the attention of medicinal chemists to finding new leads, which may later be translated into new drugs.

Chronically administered Agave americana var. marginata extract ameliorates diabetes mellitus, associated behavioral comorbidities and biochemical parameters in alloxan-induced diabetic rats.

Introduction: Diabetes mellitus causes hyperglycemia and associated complications to the brain. In current study, the traditionally reported remedial claims of Agave americana var. marginata has been scientifically investigated in diabetic rats. Methodology: The methanolic extract of leaves of Agave americana var. marginata (Aa.Cr) was characterized for total phenols, flavonoids, and antioxidant potential through in-vitro testing. The rats chronically pre-treated with Aa.Cr (400 and 600 mg/kg) for 45 days were challenged with alloxan-induced hyperglycemia. The dose-dependent effects of Aa.Cr on blood glucose levels and body weights were compared with diabetic rats using glibenclamide (0.6 mg/kg) as a standard. The animals were tested for diabetes-associated neurological comorbidities through behavioral and biochemical evaluation. Results: The phenols and flavonoids enriched Aa.Cr caused a significant dose-dependent hypoglycemic effect. Aa.Cr showed protection from comorbid anxiety, depression and cognitive impairment as compared to diabetic rats. The alanine aminotransferase, total cholesterol, triglycerides and low-density lipoprotein were prominently reduced, and high-density lipoprotein was increased in rats treated with Aa.Cr. Moreover, the oxidative stress in isolated brains was reduced by Aa.Cr. Conclusion: These findings suggest that Aa.Cr is enriched with antioxidant and anti-inflammatory phytoconstituents valuable for diabetes and related neurological complications.

Correlation of racial effect with severity of disease and in-hospital outcome in individuals diagnosed with COVID-19.

INTRODUCTION: There are many countries that inhibit diverse populations and hence, studies have been conducted to find the relation between ethnic and racial groups within a society and incidence or mortality because of coronavirus disease-19 (COVID-19). OBJECTIVES: The purpose of this study was to evaluate the racial effect on the severity of disease and in-hospital outcomes in individuals diagnosed with COVID-19. PATIENTS AND METHODS: This retrospective study is based on records of 804 tested positive COVID-19 patients presented at Dammam Medical Complex and Braira quarantine from March 2020 to May 2020 was conducted after approval from the ethical board. Patient's records included the routine patient's consent statement about the explanation of all the investigations and procedures before being performed. Data were retrieved and included in the analysis were age, gender, country of origin, racial background (Arab, Caucasian, Asian, Black, Latin and Hispanic), the severity of COVID-19 and outcome. RESULTS: Out of total 804 confirmed patients of COVID-19, there were 647 (80.5%) male patients and 157 (19.5%) female patients (M:F ratio = 4.1:1). Male preponderance was seen in all racial groups and significantly higher amongst the Asians than the Middle Eastern race (91.2% vs. 70.3%, p = .000). The mean age of Asians was significantly higher than the mean age of the Middle Eastern and Black and Caucasian races (42.8 ± 10.0 vs. 39.6 ± 16.3 vs. 37.0 ± 10.3, p = .003). The proportion of deaths was considerably higher amongst Asians (5.4%) compared with Middle Eastern patients (1.2%) (p value = .001). CONCLUSION: Severity and in-hospital outcome were varying considerably amongst the racial groups. East and South Asian COVID-19 patients had more severe symptoms and less recovery rate compared with other groups, late presentation may be a contributory reason. Hence, evaluation of the severity of COVID-19 in relation to the various racial groups along with demographic characteristics and other risk factors can provide baseline guidance to the clinical care providers to initiate earlier and appropriate treatment.

Contactless Small-Scale Movement Monitoring System Using Software Defined Radio for Early Diagnosis of COVID-19.

The exponential growth of the novel coronavirus disease (N-COVID-19) has affected millions of people already and it is obvious that this crisis is global. This situation has enforced scientific researchers to gather their efforts to contain the virus. In this pandemic situation, health monitoring and human movements are getting significant consideration in the field of healthcare and as a result, it has emerged as a key area of interest in recent times. This requires a contactless sensing platform for detection of COVID-19 symptoms along with containment of virus spread by limiting and monitoring human movements. In this paper, a platform is proposed for the detection of COVID-19 symptoms like irregular breathing and coughing in addition to monitoring human movements using Software Defined Radio (SDR) technology. This platform uses Channel Frequency Response (CFR) to record the minute changes in Orthogonal Frequency Division Multiplexing (OFDM) subcarriers due to any human motion over the wireless channel. In this initial research, the capabilities of the platform are analyzed by detecting hand movement, coughing, and breathing. This platform faithfully captures normal, slow, and fast breathing at a rate of 20, 10, and 28 breaths per minute respectively using different methods such as zero-cross detection, peak detection, and Fourier transformation. The results show that all three methods successfully record breathing rate. The proposed platform is portable, flexible, and has multifunctional capabilities. This platform can be exploited for other human body movements and health abnormalities by further classification using artificial intelligence.

Biserial Miyaguchi-Preneel Blockchain-Based Ruzicka-Indexed Deep Perceptive Learning for Malware Detection in IoMT.

Detection of unknown malware and its variants remains both an operational and a research challenge in the Internet of Things (IoT). The Internet of Medical Things (IoMT) is a particular type of IoT network which deals with communication through smart healthcare (medical) devices. One of the prevailing problems currently facing IoMT solutions is security and privacy vulnerability. Previous malware detection methods have failed to provide security and privacy. In order to overcome this issue, the current study introduces a novel technique called biserial correlative Miyaguchi-Preneel blockchain-based Ruzicka-index deep multilayer perceptive learning (BCMPB-RIDMPL). The present research aims to improve the accuracy of malware detection and minimizes time consumption. The current study combines the advantages of machine-learning techniques and blockchain technology. The BCMPB-RIDMPL technique consists of one input layer, three hidden layers, and one output layer to detect the malware. The input layer receives the number of applications and malware features as input. After that, the malware features are sent to the hidden layer 1, in which feature selection is carried out using point biserial correlation, which reduces the time required to detect the malware. Then, the selected features and applications are sent to the hidden layer 2. In that layer, Miyaguchi-Preneel cryptographic hash-based blockchain is applied to generate the hash value for each selected feature. The generated hash values are stored in the blockchain, after which the classification is performed in the third hidden layer. The BCMPB-RIDMPL technique uses the Ruzicka index to verify the hash values of the training and testing malware features. If the hash is valid, then the application is classified as malware, otherwise it is classified as benign. This method improves the accuracy of malware detection. Experiments have been carried out on factors such as malware detection accuracy, Matthews's correlation coefficient, and malware detection time with respect to a number of applications. The observed quantitative results show that our proposed BCMPB-RIDMPL method provides superior performance compared with state-of-the-art methods.

Heterojunction α-Fe2 O3 /ZnO Films with Enhanced Photocatalytic Properties Grown by Aerosol-Assisted Chemical Vapour Deposition.

Type I heterojunction films of α-Fe2 O3 /ZnO are reported here as a non-titania based photocatalyst, which shows remarkable enhancement in the photocatalytic properties towards stearic acid degradation under UVA-light exposure (λ=365 nm), with a quantum efficiency of ξ=4.42±1.54×10-4 molecules degraded/photon, which was about 16 times greater than that of α-Fe2 O3 , and 2.5 times greater than that of ZnO. Considering that the degradation of stearic acid requires 104 electron transfers for each molecule, this represents an overall quantum efficiency of 4.60 % for the α-Fe2 O3 /ZnO heterojunction. Time-resolved transient absorption spectroscopy (TAS) revealed the charge-carrier behaviour responsible for this increase in activity. Photogenerated electrons, formed in the ZnO layer, were transferred into the α-Fe2 O3 layer on the pre-µs timescale, which reduced electron-hole recombination. This increased the lifetime of photogenerated holes formed in ZnO, which oxidise stearic acid. The heterojunction α-Fe2 O3 /ZnO films grown herein show potential environmental applications as coatings for self-cleaning windows and surfaces.

Quality of assessment and counselling offered by community pharmacists and medication sale without prescription to patients presenting with acute cardiac symptoms: a simulated client study.

PURPOSE: Self-medication is common worldwide. However, the prevalence of sale of prescription medications without prescription and the quality of assessment and counselling provided by community pharmacists to cardiac patients is unknown. We sought to determine the prevalence of prescription medication sales and explore how pharmacists assess and counsel patients with acute cardiac conditions. METHODS: Six hundred community pharmacies in the two largest cities in Saudi Arabia were selected. Two simulated clients presented either an acute coronary syndrome (ACS) scenario or an acute heart failure (AHF) scenario to the pharmacists. Descriptive statistics and regression models were used to analyse and present the collected data. RESULTS: Of 600 pharmacies, 379 (63.2%) sold various prescription medications to simulated patients without prescription. Assessment and counselling provided by pharmacists were inadequate. Almost a quarter of pharmacists did not ask simulated patients any questions; 52% asked one or two questions; and only 24% asked three or more questions. Only 28 pharmacists (4.7%) inquired about drug allergies; 48.5% instructed simulated patients on the dosage and frequency of the sold medications; 21.6% provided instruction on treatment duration; and 19.4% gave instructions on dose, frequency, and duration of treatment. Compared to AHF, ACS simulated patients were more likely to be asked about other symptoms and comorbidities (59.7% vs. 48.7%, p = 0.007 and 46.3% vs. 37.3%, p = 0.005, respectively) and were more likely to be advised to go to hospital (70.3% vs. 56.3%, p < 0.001). CONCLUSIONS: The sale of prescription medications by community pharmacists to simulated cardiac patients without prescription is very common; assessment and counselling qualities are suboptimal.

Effect of absence of vision on posture.

[Purpose] The visual system is one of the sensory systems that enables the body to assess and process information about the external environment. In the absence of vision, a blind person loses contact with the outside world and develops faulty motor patterns, which results in postural deficiencies. However, literature regarding the development of such deficiencies is limited. The aim of this study was to discuss the effect of absence of vision on posture, the possible biomechanics behind the resulting postural deficiencies, and strategies to correct and prevent them. [Subjects and Methods] Various electronic databases including PubMed, Medline, and Google scholar were examined using the words "body", "posture", "blind" and "absence of vision". References in the retrieved articles were also examined for cross-references. The search was limited to articles in the English language. [Results] A total of 74 papers were shortlisted for this review, most of which dated back to the 1950s and 60s. [Conclusion] Blind people exhibit consistent musculoskeletal deformities. Absence of vision leads to numerous abnormal sensory and motor interactions that often limit blind people in isolation. Rehabilitation of the blind is a multidisciplinary task. Specialists from different fields need to diagnose and treat the deficiencies of the blind together as a team. Before restoring the normal mechanics of posture and gait, the missing link with the external world should be reestablished.

Effect of monophasic pulsed current on heel pain and functional activities caused by plantar fasciitis.

BACKGROUND: Plantar fasciitis (PF) is a soft tissue disorder considered to be one of the most common causes of inferior heel pain. The aim of this study was to investigate the effect of monophasic pulsed current (MPC) and MPC coupled with plantar fascia-specific stretching exercises (SE) on the treatment of PF. MATERIAL AND METHODS: Forty-four participants (22 women and 22 men, with a mean age of 49 years) diagnosed with PF were randomly assigned to receive MPC (n=22) or MPC coupled with plantar fascia-specific SE (n=22). Prior to and after 4 weeks of treatment, participants underwent baseline evaluation; heel pain was evaluated using a visual analogue scale (VAS), heel tenderness threshold was quantified using a handheld pressure algometer (PA), and functional activities level was assessed using the Activities of Daily Living subscale of the Foot and Ankle Ability Measure (ADL/FAAM). RESULTS: Heel pain scores showed a significant reduction in both groups compared to baseline VAS scores (P<0.001). Heel tenderness improved significantly in both groups compared with baseline PA scores (P<0.001). Functional activity level improved significantly in both groups compared with baseline (ADL/FAAM) scores (P<0.001). However, no significant differences existed between the 2 treatment groups in all post-intervention outcome measures. CONCLUSIONS: This trial showed that MPC is useful in treating inferior heel symptoms caused by PF.

On (Δm, I)-statistical convergence of order α.

The idea of I-convergence of real sequences was introduced by Kostyrko et al., (2000/01) and also independently by Nuray and Ruckle (2000). In this paper, we introduce the concepts of (Δ(m), I)-statistical convergence of order α and strong (Δ(p)(m), I)-Cesàro summability of order α of real sequences and investigated their relationship.

Weighted A-statistical convergence for sequences of positive linear operators.

We introduce the notion of weighted A-statistical convergence of a sequence, where A represents the nonnegative regular matrix. We also prove the Korovkin approximation theorem by using the notion of weighted A-statistical convergence. Further, we give a rate of weighted A-statistical convergence and apply the classical Bernstein polynomial to construct an illustrative example in support of our result.

Physicians' Perspectives on the Impact of Insurance Status on Clinical Decision-Making in Saudi Arabia.

Background The decision-making process in clinical practice depends heavily on collaboration and information sharing. Physicians' decision-making processes are profoundly influenced by the patient's insurance status, which warrants focused investigation. Hence, this study aimed to investigate how physicians perceive the influence of insurance status on treatment options and medical interventions and to explore the extent to which physicians discuss insurance-related considerations with patients during the shared decision-making process. Methodology This was a cross-sectional exploratory study conducted in various healthcare facilities all over Saudi Arabia. The electronic questionnaire was the primary tool for data collection. Data were then coded, entered, and analyzed using both descriptive and inferential statistical methods. Results The study involved 430 physicians, primarily male (n = 230, 53.5%), aged 31-40 years (n = 215, 50%), and mostly non-Saudi (n = 285, 66.3%). Medical officers constituted the majority of the study population (n = 258, 60%), with one to five years of experience (n = 187, 43.5%), and engaged in private practice (n = 230, 70%). Concerning insurance, 287 (66.7%) physicians considered patient's insurance when discussing treatment options, while 318 (74%) physicians discussed the financial implications of different treatment options with the patients. Regarding outcomes, 373 (86.7%) physicians believed that insurance status affected patient outcomes and treatment modalities. Significant factors, such as age between 31 and 40 years (P < 0.001), over 10 years of clinical experience (P = 0.002), engagement in both governmental and private practice (P = 0.012), and being a medical officer (P = 0.005), demonstrated a high impact on the insurance status influencing clinical decision-making. Overall, recognizing the influence of insurance on decision-making is crucial for equitable healthcare. Conclusions More than half of the physicians demonstrated high scores indicating the impact of insurance status on the clinical decision-making process. This impact was influenced by specific physician parameters such as age, experience, specialty, and type of practice. Moreover, the financial situation and insurance status of the patients significantly affected treatment and patient outcomes.